1. Field of the Invention
The present invention relates to automated learning of rules corresponding to the inter-relationship of data items. It is particularly, but not exclusively, applicable to learning within a neural network of a fuzzy logic system. The present invention also relates to an apparatus for such automated learning, and to a system of devices controlled according to rules, at least some of which are derived automatically by such an apparatus.
2. Summary of the Prior Art
There are many processes in which a phenomenon which varies with time is controlled by monitoring parameters of the process and controlling devices which affect those parameters. An example of such a process is water purification, but similar considerations apply to thermal, nuclear and hydraulic power generation processes, chemical processes, biological processes and even bank management information processes.
If the control of such a process is to be automated, it is necessary to define causal relationships between the variables describing the process. Traditionally, such control has therefore depended on the experience of skilled operators, but consideration has been given to the use of fuzzy logic systems which enable causal relationships to be expressed in terms which may be used by computers. Initially, the rules used by such fuzzy logic systems were established on the basis of the knowledge of skilled operators, but the use of neural networks enabled the fuzzy logic system to learn rules on the basis of the previous behavior of the system to be controlled.
Thus, EP-A-0432267 disclosed a neural network which learned (ie. derived) rules from historical data of a system such as a water purification plant. In such a system, the inter-relationships between input and output parameters were investigated and the rules of such inter-relationship determined. In this way it was possible to add to those rules established on the basis of the knowledge of a skilled operator additional rules which were implicit in the historical data derived from the operation of the system.
It has been found, however, that the practical implementation of such automated learning (automated derivation of rules) is not satisfactory. In a simple system, such as the control of an elevator or the control of a subway, the number of parameters is limited, and therefore the neural network can derive the appropriate rules within a relatively short time. However, if the process to be controlled is more complex, such as in a water purification plant or in a chemical plant, the number of relationships to be investigated increases significantly, and there are significant delays in the derivation of the appropriate rules. Hence, the application of neural networks to complex systems has proved impractical.